@Article{csse.2019.34.131,
AUTHOR = {Alberto Fernández Oliva, Francisco Maciá Pérez, José Vicente Berná-Martinez, Miguel Abreu Ortega},
TITLE = {Non-Deterministic Outlier Detection Method Based on the Variable Precision Rough Set Model},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {34},
YEAR = {2019},
NUMBER = {3},
PAGES = {131--144},
URL = {http://www.techscience.com/csse/v34n3/40034},
ISSN = {},
ABSTRACT = {This study presents a method for the detection of outliers based on the Variable Precision Rough Set Model (VPRSM). The basis of this model is the
generalisation of the standard concept of a set inclusion relation on which the Rough Set Basic Model (RSBM) is based. The primary contribution of this
study is the improvement in detection quality, which is achieved due to the generalisation allowed by the classification system that allows a certain degree
of uncertainty. From this method, a computationally efficient algorithm is proposed. The experiments performed with a real scenario and a comparison of
the results with the RSBM-based method demonstrate the effectiveness of the method as well as the algorithm’s efficiency in diverse contexts, which also
involve large amounts of data.},
DOI = {10.32604/csse.2019.34.131}
}